While visual data capture (e.g., image or video capture) has been utilized for a variety of purposes, new technologies are starting to make available new and/or improved utilization. For example, data analysis operations may be performed on visual data to extract features of the visual data. Feature recognition, motion detection, etc. may be employed for a variety of uses. Face detection may be used in robotics to allow robots to locate faces, identify people, etc. Moreover, image and/or video capture may commonly occur at transportation hubs (e.g., airports, train terminals, bus stations, etc.), entertainment venues (e.g., stadiums, arenas, theatres, etc.), medical service providers (e.g., hospitals, drug dispensaries, etc.), educational and governmental institutions, commercial locations, etc. At least one use for image capture in these locations is security. For example, image and/or video data may be reviewed on the occurrence of an incident to determine what happened, who was involved, how the situation should be resolved, etc. Feature recognition such as facial detection may be used to analyze the image/video data to determine actors/factors that caused the event, victims, rescuers, etc.
Algorithms that are currently available for face detection and tracking may include, for example, color extraction, motion detection, model-based face tracking, edge-orientation tracking, weak classifier cascades, etc. A widely used version of weak classifier cascades is the Viola-Jones Object Detection Framework. In the Viola-Jones algorithm, image pixels in rectangular areas or “sub-windows” within an image/video may be summed. The difference between the summation of the light and dark areas may be indicative of certain features (e.g., certain combinations of light and dark areas) that may be indicative of a human face. While this manner of face detection may be effective, it is greatly affected by the amount of pixels being evaluated. For example, a 640×480 image may comprise approximately 1.4 million sub-windows for evaluation. Depending on the capabilities of the analysis system, having to analyze such a large amount of sub-windows may place substantial processing burden on the analysis system, which may then take longer to perform facial detection. Moreover, image and/or video capture technology is constantly improving, which means that the pixel size of images and/or video constantly increasing, placing a larger burden on the analysis system.
Although the following Detailed Description will proceed with reference being made to illustrative embodiments, many alternatives, modifications and variations thereof will be apparent to those skilled in the art.